Describing a complex primary health care population in a learning health system to support future decision support and artificial intelligence initiatives

IntroductionLearning health systems (LHS) use data to improve care. Descriptive epidemiology to reveal health states and needs of the LHS population is essential for informing LHS initiatives, including development of decision support tools. To properly characterize complex populations, both simple statistical and artificial intelligence techniques can be useful. We present the first large-scale description of the population served by one of the first primary care LHS in North America. ObjectivesOur objective is to describe sociodemographic, clinical, and health care use characteristics of adult primary care clients served by the Alliance for Healthier Communities, which provides team-based primary health care through Community Health Centres (CHCs) across Ontario, Canada. MethodsUsing electronic health record data from 2009-2019 for all CHCs, we perform table-based summaries for each characteristic; and apply unsupervised leaning techniques to explore patterns of common condition co-occurrence, care provider teams, and care frequency. ResultsOf the 221,047 eligible clients, those at CHCs that primarily serve those most at risk (homeless, mental health, addictions) tend to have more chronic conditions and social determinants of health, which are also prominent in clients with multimorbidity. Most care is provided by physician and nursing providers, with heterogeneous combinations of other provider types. A subset of clients have many issues addressed within single-visits and there is within- and between-client variability in care frequency. Example methodological considerations learned for future LHS initiatives include the need to carefully consider the level of analysis and associated implications for data quality and target population, heterogeneity in conditions and care characteristics, and non-uniform risk profiles across the care history. ConclusionsWe demonstrate the use of methods from statistics and artificial intelligence, applied with an epidemiological lens, to provide an overview of a complex primary care population. In addition to substantive findings, we discuss implications for future LHS initiatives.